GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Tyna Eloundou OpenAI Sam Manning OpenAI OpenResearch Pamela Mishkin Corresponding author Authors contributed equally and are listed alphabetically. OpenAI Daniel Rock University of Pennsylvania
Abstract
We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share in
中文速览
大约80%的美国劳动力将有至少10%的工作任务受到大型语言模型（LLMs，如GPT系列）的影响，而近19%的工人可能有超过一半的任务被波及——这是研究者用一套全新的"曝光度"评估框架，结合人工标注与GPT-4自动分类，对O*NET职业数据库中数百种职业逐项任务打分后得出的结论。研究发现，单靠现有语言模型本身，约15%的美国工人任务可以在保持质量的前提下显著提速，而一旦纳入基于LLM构建的配套软件与工具，这一比例跃升至47%至56%。与以往自动化研究不同的是，高薪职业反而面临更高的曝光风险，且影响并不集中于近年生产率增长较快的行业，说明LLM的冲击将是跨行业、跨收入层次的广泛渗透。这项研究最终指出，GPT类模型具备"通用目的技术"的特征，其经济与社会影响的深度和广度可能远超以往任何单一自动化浪潮，值得政策制定者高度关注。
原文 arXiv:2303.10130;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2303.10130v5